Bergmann Tobias, Vakitbilir Nuray, Gomez Alwyn, Islam Abrar, Stein Kevin Y, Sainbhi Amanjyot Singh, Froese Logan, Zeiler Frederick A
Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada.
Bioengineering (Basel). 2024 Sep 18;11(9):933. doi: 10.3390/bioengineering11090933.
Artifacts induced during patient monitoring are a main limitation for near-infrared spectroscopy (NIRS) as a non-invasive method of cerebral hemodynamic monitoring. There currently does not exist a robust "gold-standard" method for artifact management for these signals. The objective of this review is to comprehensively examine the literature on existing artifact management methods for cerebral NIRS signals recorded in animals and humans. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search yielded 806 unique results. There were 19 articles from these results that were included in this review based on the inclusion/exclusion criteria. There were an additional 36 articles identified in the references of select articles that were also included. The methods outlined in these articles were grouped under two major categories: (1) motion and other disconnection artifact removal methods; (2) data quality improvement and physiological/other noise artifact filtering methods. These were sub-categorized by method type. It proved difficult to quantitatively compare the methods due to the heterogeneity of the effectiveness metrics and definitions of artifacts. The limitations evident in the existing literature justify the need for more comprehensive comparisons of artifact management. This review provides insights into the available methods for artifact management in cerebral NIRS and justification for a homogenous method to quantify the effectiveness of artifact management methods. This builds upon the work of two existing reviews that have been conducted on this topic; however, the scope is extended to all artifact types and all NIRS recording types. Future work by our lab in cerebral NIRS artifact management will lie in a layered artifact management method that will employ different techniques covered in this review (including dynamic thresholding, autoregressive-based methods, and wavelet-based methods) amongst others to remove varying artifact types.
患者监测期间产生的伪影是近红外光谱技术（NIRS）作为一种无创脑血流动力学监测方法的主要限制因素。目前尚不存在针对这些信号的稳健的伪影管理“金标准”方法。本综述的目的是全面考察有关动物和人类记录的脑NIRS信号现有伪影管理方法的文献。根据系统评价和Meta分析的首选报告项目指南，对五个数据库进行了检索。检索结果共806条。根据纳入/排除标准，这些结果中有19篇文章被纳入本综述。在部分文章的参考文献中还确定了另外36篇文章也被纳入。这些文章中概述的方法分为两大类：（1）运动和其他断开伪影去除方法；（2）数据质量改善和生理/其他噪声伪影滤波方法。这些方法按方法类型进一步细分。由于有效性指标和伪影定义的异质性，难以对这些方法进行定量比较。现有文献中明显的局限性证明有必要对伪影管理进行更全面的比较。本综述深入探讨了脑NIRS中可用的伪影管理方法，并为量化伪影管理方法有效性的统一方法提供了依据。这是在之前针对该主题进行的两项现有综述的基础上开展的工作；然而，范围扩展到了所有伪影类型和所有NIRS记录类型。我们实验室未来在脑NIRS伪影管理方面的工作将采用分层伪影管理方法，该方法将采用本综述中涵盖的不同技术（包括动态阈值处理、基于自回归的方法和基于小波的方法）等，以去除不同类型的伪影。
